# Senior Applied Scientist, Amazon Global Data Center Ops Central Insight and Analytics Team at Amazon

Amazon is hiring a Senior Applied Scientist, Amazon Global Data Center Ops Central Insight and Analytics Team in Seattle, United States. It pays $167k-$226k a year and Level rates it Builds AI ●●●●; you can [apply on Amazon](https://jobsbylevel.com/go/39e238dd-a256-4323-b933-465ce1e4128e?utm_medium=ai-agent).

AI Level 4, AI centrality 90 out of 100. US, WA, Seattle.

## Details

- Company: [Amazon](https://jobsbylevel.com/companies/amazon)
- AI level: AI Level 4 (score 90 out of 100)
- Location: US, WA, Seattle
- Salary: $167k-$226k
- Posted: August 25, 2026
- Apply: https://jobsbylevel.com/go/39e238dd-a256-4323-b933-465ce1e4128e?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-applied-scientist-amazon-global-data-center-ops-central-insight-and-analytics-team-at-amazon-208c1a

## Prepare for this job

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## Description

We are looking for an seasoned Applied Scientist to design, build, and deploy the ML/AI models that power our decision intelligence platform. You will work at the intersection of causal inference, time-series forecasting, anomaly detection, and LLM-based reasoning — all applied to real operational problems with measurable business impact. Key Job Responsibilities Decision Intelligence Models - **Causal inference & root cause analysis:** Build models that decompose fleet-wide metric movements into root causes, distinguishing correlation from causation across operational dimensions (site, service, failure mode, time) - **Dose-response modeling:** Develop models that learn the quantitative relationship between intervention intensity and outcome magnitude - **Forecasting & projection:** Build time-series models that project metric trajectories under different intervention scenarios, enabling "if we do X, expect Y by date Z" recommendations - **Anomaly detection & trend identification:** Develop multi-variate anomaly detection that distinguishes signal from noise in noisy operational data, and identifies emerging patterns before they become crises - **Confidence calibration:** Build and maintain calibrated confidence scores for recommendations, ensuring the system knows what it knows and what it doesn't - **Outcome attribution:** Design experiments and causal methods to measure the true impact of interventions LLM Integration & Reasoning - **Structured reasoning:** Design LLM prompting architectures that reliably transform operational data into executive-quality narrative summaries, decision framings, and recommendation rationales - **LLM evaluation:** Build evaluation frameworks that measure LLM output quality (accuracy, actionability, calibration) and detect degradation over time - **RAG systems:** Design retrieval-augmented generation systems that ground LLM outputs in operational data, historical playbooks, and institutional knowledge - **Progressive autonomy:** Design the trust-calibration system where AI gradually earns expanded authority based on demonstrated accuracy over time Research & Production - **End-to-end ownership:** Take models from research through production deployment — you ship, you monitor, you iterate - **Experimentation:** Design A/B tests and quasi-experiments to validate model improvements and measure business impact - **Stakeholder communication:** Translate complex scientific results into actionable insights for non-technical senior leaders Basic qualifications - 3+ years of building machine learning models for business application experience - PhD in Machine Learning, Statistics, Computer Science, Operations Research, or related quantitative field (or Master's + 4 years of applied science experience) - Strong expertise in at least two of: causal inference, time-series forecasting, anomaly detection, NLP/LLMs - Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn, statsmodels) - Experience with experimental design and causal methods (difference-in-differences, synthetic control, instrumental variables, or Bayesian causal inference) - Experience deploying ML models to production (not just research/notebooks) - Track record of publications or equivalent internal research contributions Preferred qualifications - Experience in building machine learning models for business application - Experience with LLM integration (prompt engineering, RAG, fine-tuning, evaluation frameworks) - Experience with dose-response modeling, treatment effect estimation, or pharmacometric-style modeling - Experience with operational/infrastructure data (time-series at scale, noisy signals, multi-dimensional hierarchies) - Experience with Bayesian methods (probabilistic programming, uncertainty quantification) - Background in supply chain optimization, capacity planning, or operations research - Experience building decision support systems that serve non-technical stakeholders - Experience measuring

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/senior-applied-scientist-amazon-global-data-center-ops-central-insight-and-analytics-team-at-amazon-208c1a

Source: https://jobsbylevel.com/jobs/senior-applied-scientist-amazon-global-data-center-ops-central-insight-and-analytics-team-at-amazon-208c1a

## Cite this page

Level. https://jobsbylevel.com/jobs/senior-applied-scientist-amazon-global-data-center-ops-central-insight-and-analytics-team-at-amazon-208c1a.

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